Conventional approaches to detect objects in digital images, such as to detect faces in an image, generally utilize a model-training approach to develop discriminative classifiers off-line, and then scan an image in multiple region segments comparing the discriminative classifiers of the trained model to faces or other objects in the image. A current exemplar-based face detector models faces using a set of exemplars directly, and to detect faces in an image, every exemplar is applied as an individual voting map over the image. The individual voting maps are aggregated after thresholding to build an overall voting map for the image, and then the location of faces in the image can be located by detecting peaks in the overall voting map. An advantage of this exemplar-based face detector is its robustness to large appearance variations. However, despite the effectiveness, a large number of exemplars are needed by the exemplar-based face detector, which makes it unpractical for use in terms of processing speed and memory used to compute the large number of exemplars.